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• W2952054750 abstract "The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset." @default.
• W2952054750 created "2019-06-27" @default.
• W2952054750 creator A5030013057 @default.
• W2952054750 creator A5071967339 @default.
• W2952054750 date "2019-03-01" @default.
• W2952054750 modified "2023-09-25" @default.
• W2952054750 title "Image-Based Geo-Localization Using Satellite Imagery" @default.
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• W2952054750 doi "https://doi.org/10.48550/arxiv.1903.00159" @default.
• W2952054750 hasPublicationYear "2019" @default.
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